In fixed rate coding a block of n symbols must be encoded using r bits where r is a fixed number. The rate distortion optimal solution for this problem is to use a fixed rate vector quantization with a codebook of size 2r. This approach is computationally expensive and instead a fixed rate scalar quantization can be used. However, scalar quantization results in relatively poor performance. —One of the common solutions is to use entropy coded scalar quantization. In this technique symbols are quantized using a scalar quantizer and the quantized symbols are entropy coded. The quantization step size must be adjusted so that the entropy coded symbols can be coded using fewer than r bits. The resulting bits are placed in a packet that has a fixed size of r bits.
If a sequence of symbols cannot be encoded using fewer than r bits more quantization is applied and the amount of information that is sent is reduced. Therefore, the best coding algorithm maximizes the probability of encoding sequences of symbols using fewer than r bits. However entropy coding algorithms like Huffman coding or arithmetic coding minimize the average bit rate and therefore may not be optimal for fixed rate compression algorithms.
Accordingly, a new system and method are needed that improve compression performance for fixed rate compression algorithms.